99
95
90
80
70
60
50
40
30
20
10
5
1
Standardized Residual
Percent
Normal Probability Plot
(response is Price ($))
60
59
58
57
56
55
54
53
52
2
1
0
-1
-2
Fitted Value
Standardized Residual
Versus Fits
(response is Score)

3
2
1
0
-1
-2
-3
99
95
90
80
70
60
50
40
30
20
10
5
1
Standardized Residual
Percent
Normal Probability Plot
(response is Score)
7.0
6.5
6.0
5.5
5.0
4.5
4.0
65
60
55
50
45
40
Weight (oz.)
Score
Scatterplot of Score vs Weight (oz.)

16
15
14
13
12
11
10
65
60
55
50
45
40
Megapixels
Score
Scatterplot of Score vs Megapixels
400
350
300
250
200
150
100
70
65
60
55
50
45
40
Price ($)
Score
Scatterplot of Score vs Price ($)
Results
The results show that there is little correlation between a cameras megapixels and weight
affecting the price and score of the camera. R
2
is a measure of how close the data is to the fitted

line of regression, or how much the model explains or doesn’t explain the variability of the
response data around its mean. The resulting r
2
for the regression analysis for price ($) versus
megapixels and weight was 16.68%. The r
2
for the regression analysis for score versus
megapixels and weight resulted in 8.41%. These regression scores show that very little of the
variability in price and score are explained by the predictors (weight and megapixels).
These values show that price and score are influenced very little to almost none by
megapixels and weight. Also, the Standard error of regression (S) shows that for price versus
Megapixels and weight, the average distance from the line of regression is 78.54. For Score
versus megapixels and weight, the average distance from the line of regression is 6.65. S shows
how wrong, on average, the regression model is using the response variable. Smaller values are
better because they are closer to the fitted line.
Price seems to be the best overall predictor of score because the scatterplots show that
there is little deviation from the line of regression, resulting in a much higher r
2
value than any
other variable assessed in the study.
Score is influenced by price, which can be seen in the
regression analysis.
It shows very little departure from the line of regression.
Regression Analysis: Score versus Price ($)
Analysis of Variance
Source
DF
Adj SS
Adj MS
F-Value
P-Value
Regression
1
311.66
311.660
23.80
0.000
Price ($)
1
311.66
311.660
23.80
0.000
Error
11
144.03
13.094
Lack-of-Fit
6
104.78
17.464
2.22
0.199
Pure Error
5
39.25
7.850
Total
12
455.69
Model Summary
S
R-sq
R-sq(adj)
R-sq(pred)
3.61854
68.39%
65.52%
53.13%
Coefficients
Term
Coef
SE Coef
T-Value
P-Value
VIF
Constant
47.29
2.57
18.38
0.000
Price ($)
0.0665
0.0136
4.88
0.000
1.00
Regression Equation
Score = 47.29 + 0.0665 Price ($)
Fits and Diagnostics for Unusual Observations
Obs
Score
Fit
Resid
Std Resid
13
46.00
53.27
-7.27
-2.21
R
R
Large residual